Search Results (286)

Photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and light-induced thermoelastic spectroscopy (LITES) represent indirect absorption spectroscopy techniques for trace gas sensing, whose performance has long been advanced through hardware-oriented enhancement strategies. However, as hardware technologies continue to advance, conventional hardware-based enhancements are increasingly bottlenecked by weak responses, complex cross-interferences, and coupled multiphysics parameters. To transcend these limitations, algorithm-assisted methods, including traditional algorithms, machine learning, deep learning, and intelligent optimization, are being systematically integrated into these spectroscopic systems. This review summarizes recent progress in intelligent indirect absorption spectroscopy from three interconnected dimensions. First, we outline advanced signal processing and spectral reconstruction strategies designed to achieve weak-signal recovery and background noise suppression. Second, the focus shifts to data-driven parameter inversion, showing how multidimensional artificial intelligence models contribute to concentration retrieval, environmental compensation, multicomponent recognition, spectral-overlap decoupling, and front–back-end collaborative waveform coding and demultiplexing. Third, intelligent system optimization is examined, in which surrogate modeling, swarm-intelligence search, physics-guided topology optimization and multi-objective algorithms are employed to improve the design efficiency of the key elements such as photoacoustic resonators and multipass cells (MPCs). Additionally, prospects for future technological developments are also discussed in the concluding section. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

Necrotizing enterocolitis (NEC) remains one of the most serious gastrointestinal emergencies in preterm infants, and imaging plays a central role in diagnosis and clinical management. Historically, evaluation has relied primarily on abdominal radiography, which remains widely available and embedded in established diagnostic frameworks. However, the hallmark radiographic signs of NEC (i.e., pneumatosis intestinalis, portal venous gas, and free air) reflect relatively advanced manifestations of intestinal injury that indicate established mucosal disruption or transmural necrosis. Bowel ultrasound has increasingly complemented radiography by enabling real-time assessment of bowel wall integrity, perfusion, motility, and intra-abdominal fluid, providing physiologic information that may refine clinical interpretation and monitoring of disease progression. Expanding use of neonatologist-performed bowel ultrasound may further improve access to bedside intestinal imaging and facilitate more timely evaluation in neonatal intensive care settings. In parallel, emerging imaging technologies seek to extend the capabilities of conventional imaging by interrogating biologic processes that underlie intestinal injury. Modalities such as contrast-enhanced ultrasound, ultra-high-frequency ultrasound, and photoacoustic imaging offer the potential to characterize bowel microvascular perfusion, tissue oxygenation, and microstructural changes that may precede overt radiographic abnormalities. Complementary physiologic monitoring approaches are also being explored to identify infants at risk before clinical disease develops. Techniques including superior mesenteric artery Doppler, near-infrared spectroscopy, bowel acoustic monitoring, and electrogastrography aim to detect early alterations in intestinal perfusion, oxygenation, and motility. In addition, artificial intelligence applied to imaging and physiologic data may enhance pattern recognition, risk stratification, and clinical decision support. Together, these advances suggest that NEC evaluation is evolving from a paradigm focused on detecting late structural injury toward integrated approaches capable of identifying intestinal vulnerability earlier and monitoring disease more precisely. Full article

Dissolved gas analysis (DGA) is an essential technique for the fault diagnosis and condition monitoring of oil-immersed power transformers. Among various characteristic gases, acetylene (C₂H₂) is a key indicator of high-energy discharge and arc faults. In this work, a high-sensitivity dissolved acetylene detection system is developed based on clamp-type quartz-enhanced photoacoustic spectroscopy (QEPAS). A specially designed clamp-type quartz tuning fork (Clamp-type QTF) is employed as the acoustic transducer to improve acoustic coupling efficiency and optical alignment tolerance. Compared with conventional standard quartz tuning forks, the clamp-type structure exhibits enlarged acoustic interaction volume, lower damping loss, and higher signal collection capability. A near-infrared distributed feedback (DFB) laser operating at 1531.6 nm is used as the excitation source. The dissolved gas is extracted from transformer oil using a headspace degassing module and introduced into the QEPAS cell for real-time measurement. Experimental results showed that the developed system achieves a 1σ-based SNR-estimated detection limit of 17 ppb at a 50 s integration time, derived from the continuous measurement of 0.75 ppm C₂H₂, with excellent linearity in the concentration range from 100 ppm to 500 ppm. The measured concentration of dissolved acetylene in transformer oil is in good agreement with gas chromatography (GC), validating the effectiveness and practical applicability of the proposed system. Full article

In the world of electronics, sensors are more than just components—they are the eyes, ears, and touchpoints of modern technology. From self-driving cars that rely on LiDAR and ultrasonic sensors to navigate complex environments, smart watches that detect your every move and heartbeat, to advanced brain chip implants that can sense your thoughts and translate them into physical moves with the assistance of exoskeletons, sensors bridge the gap between the physical world and digital systems. The rapid arrival of advanced Artificial Intelligence (AI) and Large Language Models (LLMs) has transformed almost every part of technology, especially data processing. However, the development of sensors remains a vitally important topic. Sensors form the foundation of innovation in electronics; novel sensors provide reliable data across a broad range of application areas and are a foundation for intelligent systems. Notably, knowing the capabilities and limitations of each sensor type is crucial for selecting the right sensor for a specific application, troubleshooting issues, and optimizing system performance. This book, entitled “Advanced Sensors for Real-Time Monitoring Applications II”, demonstrates developments of real sensors for a range of applications, including descriptions of fundamental principles of operation, concepts, theory, and practical validation of the results, as well as a review of current state-of-the-art and future directions. Full article

According to the International Diabetes Federation, 589 million adults worldwide live with diabetes in 2025 (approximately 1 in 9 adults). The development of convenient noninvasive blood glucose monitoring systems has been a central focus in diabetes management. Optical spectroscopy has advanced significantly among all noninvasive glucose detection techniques. A photoacoustic system has been developed using a single-wavelength near-infrared laser, operating at 1625 nm, where glucose exhibits an overtone absorption band with relatively low water interference. The noninvasive system has been evaluated using artificial skin phantoms, with different glucose concentrations, covering both normoglycemic and hyperglycemic blood glucose levels. The detection sensitivity of the developed system has been enhanced to ±15 mg/dL across the entire clinically relevant glucose range. K-nearest neighbours and wide neural network machine learning models were developed for noninvasive glucose classification. The models achieved prediction accuracies of 80.0% and 81.5%, respectively, with 100% of the predicted data located within zones A and B of Clarke’s error grid analysis. These findings satisfy the regulatory requirements for glucose monitors established by Health Canada and the U.S. Food and Drug Administration. Full article

Glioblastoma (GBM) remains the most aggressive primary malignant brain tumor in adults, with poor survival largely driven by diffuse cellular infiltration, profound heterogeneity, and near-universal recurrence following standard therapy. Although maximizing the extent of resection is a key determinant of patient outcome, current clinical imaging modalities lack the spatial resolution necessary to detect microscopic tumor invasion and therapy-resistant cell populations. Emerging in vivo imaging technologies capable of cellular and near-single-cell resolution have therefore become a major focus in preclinical neuro-oncology research, with growing relevance for surgical guidance, treatment adaptation, and translational discovery. This review evaluates multiple optical imaging modalities, including multi-photon microscopy, near-infrared II fluorescence imaging, bioluminescence imaging, photoacoustic imaging, optical coherence tomography, confocal laser endomicroscopy, Raman spectroscopy, autofluorescence microscopy, and fluorescence macroscopy with a focus on their ability to detect residual GBM cells. Despite significant advances, these approaches remain constrained by limitations in molecular target availability, probe delivery across the blood–brain barrier, and signal variability within heterogeneous tumor regions. The biological complexity of GBM further challenges detection, as residual tumor cells are spatially dispersed and phenotypically diverse, limiting the effectiveness of single-marker or single-modality strategies. Together, these findings highlight the need for integrated, biologically informed imaging approaches to improve detection of residual disease and guide surgical decision making. Full article

Organic dye-, pharmaceutical-, and heavy metal-contaminated water are emerging environmental issues, and thus there is a requirement for the development of efficient and sustainable purification methods. Semiconductor (SmC) material-based photocatalysis using TiO₂ and Fe₂O₃ nanostructures is considered a promising field for pollutant degradation due to its chemical stability, nontoxicity, and ability to perform photocatalytic degradation using light irradiation. Understanding the thermal, optical, and charge transport properties governing their photocatalytic activity requires advanced characterisation methods. In this context, photothermal (PT) techniques provide powerful tools for probing non-radiative processes and energy transport in photocatalytic materials. The photocatalytic activity of these materials strongly depends on their structural, optical, thermal, and electronic properties. These properties can be enhanced through several modification strategies, including metal and non-metal doping (e.g., C, N, Cu, Ag, Au), surface modification, forming a complex with SiO₂, and the formation of Fe₂O₃–TiO₂ heterostructure nanocomposites. In this review, a comprehensive overview is provided of TiO₂ and Fe₂O₃-based nanocomposites with a specific focus on characterisation techniques for photothermal characterisation techniques, including thermal lens spectroscopy (TLS), beam deflection spectrometry (BDS), and photoacoustic spectroscopy (PAS), for determining thermal diffusivity, thermal conductivity, bandgap energy, carrier lifetime, surface roughness, porosity, etc., which are related to photocatalytic activity. The properties of these nanocomposites are correlated with photocatalytic activity for pollutant degradation using these nanocomposites. The challenges faced while using these nanocomposites for pollutant degradation are also discussed, along with future prospects for designing efficient photocatalysts for water purification applications. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

In this work we investigate the behavior of a toroidal photoacoustic resonator to provide compact, physics-guided analytical relationships that link its geometry to two key parameters: resonance frequency and quality factor. Finite-element data are combined with reduced-order analytical models to refine a corrected toroidal-resonance frequency model that accounts for effective propagation length and thermo-viscous effects. For the quality factor, a simple law motivated by a boundary-layer dissipation model is proposed. Derived models are validated by experimental tests performed using three 3D printed toroidal resonators in different sizes. Experimental results confirm the prediction both for the first and third resonance frequencies with an average relative error below 1%, outperforming cylindrical and uncorrected baseline models available in the literature. The results also confirm the predicted trend of the quality factor with respect to the torus’s minor radius, highlighting a direct relationship between the cross-sectional area and acoustic losses, which governs the balance between stored acoustic energy and thermo-viscous dissipation. Overall, the framework provides quick, interpretable design rules that reduce dependence on extensive finite-element method simulation campaigns for first-pass estimation of resonant behavior during the early design phase and guiding the optimization of high-performance PAS devices while preserving accuracy. Full article

Formaldehyde (H₂CO) is a hazardous volatile organic compound widely present in indoor and industrial environments, and its real-time, highly sensitive detection is essential for environmental safety. However, existing detection techniques often face challenges in simultaneously achieving high sensitivity and long-term stability, and many conventional photoacoustic spectroscopy (PAS) systems rely strongly on low gas flow rates to suppress flow-induced noise, which limits their applicability for continuous online monitoring. In this work, an ultraviolet photoacoustic spectroscopy (UV-PAS)-based H₂CO detection system operating in a nitrogen (N₂) background is developed. The system integrates a compact differential photoacoustic cell (PAC) with a 320 nm ultraviolet laser source, in which the resonator length and buffer configuration are carefully optimized to enhance acoustic resonance and effectively suppress flow-related disturbances. Notably, a key innovation of this study is that the system maintains a stable photoacoustic response even under relatively high gas flow conditions. Experimental results demonstrate that at a flow rate of 250 sccm, the photoacoustic signal amplitude remains stable, and the noise level is well controlled, significantly reducing the dependence of conventional PAS systems on low-flow operation. The photoacoustic cell exhibits a resonant frequency of 1767 Hz and a quality factor of 46. Calibration using a 47.31 ppm H₂CO:N₂ gas mixture shows a good linear response with a correlation coefficient of R² = 0.98844. The minimum detection limit reaches 2.50 ppm at a 1 s integration time and is further improved to 88.1 ppb at an integration time of 2202 s based on Allan–Werle deviation analysis. These results demonstrate that the proposed UV-PAS system provides a sensitive, stable, and cost-effective solution for real-time trace H₂CO detection while retaining robust performance at elevated gas flow rates, highlighting its strong potential for practical applications. Full article

We report a deep learning-assisted quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor for trace water vapor detection in air. A 1392 nm butterfly-packaged DFB laser is wavelength-modulated at f₀/2, and the QEPAS signal is retrieved by second-harmonic (2f) lock-in demodulation using a commercial quartz tuning fork gas cell. After optimizing the modulation depth to 400 mV, a 1D U-Net denoising network trained with pseudo-clean supervision is applied to the measured 2f traces, yielding an SNR improvement of 2.05× (3.11 dB). Allan deviation analysis indicates a minimum detection limit (MDL) of ~2.21 ppm at an optimum averaging time of ~619 s, corresponding to an ~2.1× improvement compared with the raw output. These results demonstrate that neural-network-based post-processing can improve QEPAS water vapor sensing performance without modifying the optical hardware. Full article

The field of developing effective catalysts for heterogeneous catalysis has recently focused on controlling the structures of catalysts themselves to optimise the density and energy of crystal lattice defects. This can significantly influence catalytic activity in terms of both reaction rates and reaction mechanisms, and thus the selective production of desired substances as well. In many cases, these crystal lattice defects manifest themselves as so-called electron traps (ETs) and thus significantly influence charge transfer between the catalyst and reactants. ETs provide the missing electronic link between atomic-scale defects and macroscopic performance in heterogeneous catalysis. Therefore, the importance of ETs for catalysis is particularly evident in areas where charge transfer plays a fundamental role in the reaction mechanism, such as photocatalysis and electrocatalysis. In the field of thermally initiated reactions, the importance of ETs in heterogeneous catalysis has not yet been fully appreciated. However, several studies have already addressed the importance of ETs for this type of reaction. This review consolidates and extends the concept of ETs to purely thermal-initiated reactions, with a focus on CO₂ hydrogenation using typical transition metal catalysts. Firstly, in this review, ETs are defined as band gap states associated with internal and external defects, and their depth, density, spatial location, and dynamics are then coupled with key steps in thermocatalytic cycles, including charge storage/release, reactant activation, intermediate stabilisation, and redox turnover. Secondly, electron trap detection is reviewed based on advanced spectroscopic techniques, including reversed double-beam photoacoustic spectroscopy (RDB-PAS), thermally stimulated current (TSC), deep-level transient spectroscopy (DLTS), thermoluminescence (TL), electron paramagnetic resonance (EPR), and photoluminescence (PL), highlighting how each method describes trap energetics and populations under realistic operating conditions. Finally, case studies on the application of metal oxides and supported metals are discussed, as these are typical catalysts for the reaction mentioned above. This review highlights how oxygen vacancies (OVs), polarons, and metal–support interfacial sites act as robust electron reservoirs, lowering the barriers for CO₂ activation and hydrogenation. By reframing thermocatalysts through the lens of ET chemistry, this review identifies ETs as actionable targets for the rational design of next-generation materials for CO₂ hydrogenation and related high-temperature transformations. Full article

In this work, a carrier-free photoacoustic spectroscopy system is developed for the detection of trace acetylene gas in insulating oil. The photoacoustic cell was integrated with an oil–gas separator, allowing dissolved gases in oil to be introduced into the cell through free diffusion. The oil–gas separator is a custom-fabricated AF2400-coated ceramic membrane, and its spin-coating process was carefully designed to enable rapid oil–gas separation and achieve high film flatness. Using a resonant photoacoustic cell and a low-noise lock-in amplifier, the sensitivity of the system was improved to 6.90 mV/ppm, with a repeatability error less than 1.65%. Calibration experiments demonstrated that continuous detection of dissolved gas in oil could be achieved, with a response time T₉₀ of less than 72.5 min. Compared to traditional photoacoustic spectroscopy, the continuous measurement capability of this method is expected to enable earlier fault diagnosis, thus having greater potential in industrial fields. Full article

In the paper, a heterodyne quartz-enhanced photoacoustic spectroscopy (H-QEPAS)-based integrated methane (CH₄) sensor prototype is reported. The CH₄ absorption line located at 1650.96 nm was selected as the target spectral line. The design features an integrated, 3D-printed gas chamber for reduced size and weight. To realize the coordinated operation of each hardware component, a control program was designed based on LabVIEW platform, enabling the adjustment of various hardware parameters. The piezoelectric signal generated by the quartz tuning fork (QTF) was amplified via a trans-impedance amplifier (TIA), acquired by a data acquisition card (DAQ), and then transmitted to a virtual lock-in amplifier (LIA) on the PC terminal for processing. The dimensions of the integrated CH₄ sensor prototype are 33 cm in length, 27 cm in width, and 15 cm in height. The final test results demonstrate that the sensor prototype exhibits an excellent concentration linear response, with a detection limit of 26.72 ppm and a short detection time of approximately 4 s. Full article